Business Analytics

The units of study listed in the following table are those available for the current. Students may also include any units of study, which are additional to those currently listed, which appear under these subject areas in the Business School handbook/website in subsequent years (subject to any prerequisite or prohibition rules).

Unit outlines will be available through Find a unit outline two weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
 

Table A - The University of Sydney Business School

Business Analytics

1000-level units of study

BUSS1020 Quantitative Business Analysis

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prohibitions: ECMT1010 or MATH1005 or MATH1905 or MATH1015 or STAT1021 or ENVX1001 or ENVX1002 or DATA1001 or MATH1115 Assumed knowledge: Mathematics (equivalent of band 4 in the NSW HSC subject Mathematics or band E3 in Mathematics Extension 1 or 2) OR MATH1111 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
All graduates from the BCom need to be able to use quantitative techniques to analyse business problems. This ability is important in all business disciplines since all disciplines deal with increasing amounts of data, and there are increasing expectations of quantitative skills. This unit shows how to interpret data involving uncertainty and variability; how to model and analyse the relationships within business data; and how to make correct inferences from the data (and recognise incorrect inferences). The unit will include instruction in the use of software tools (primarily spreadsheets) to analyse and present quantitative data.
QBUS1040 Foundations of Business Analytics

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH 1000-level units which must include MATH1905 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit provides students with the necessary foundations and skills to undertake second year units in business analytics and successfully complete the Business Analytics major. Theoretical models discussed are motivated by real-life business applications and decision problems. The unit provides a grounding in linear algebra (matrix properties) and calculus and applies these methods to regression models with multiple variables. Topics covered include logistic regression, interaction and nonlinear effects. The unit also introduces the key ideas of optimization (particularly for quadratic problems) and shows how optimisation models can be used to make statistical estimates. At the same time as building the understanding of the mathematical foundations needed in business analytics, the unit helps students to build programming skills to solve practical problems from the business area. The unit makes use of modern programming languages such as Python.

2000-level units of study

QBUS2310 Management Science

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: Students commencing from 2018: QBUS1040; Pre-2018 commencing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: ECMT2620 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
The ability to understand and mathematically formulate decision problems is a fundamental skill for managers in any organisation. This unit focuses on basic management science modelling techniques used in capacity planning, production management, and resource allocation. Students learn to approach complex real-life problems, formulate appropriate models and offer solution procedures to ensure optimal use of resources. Methods include linear programming, integer programming, quadratic programming, and dynamic programming.
QBUS2810 Statistical Modelling for Business

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: Students commencing from 2018: QBUS1040; Pre-2018 continuing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: ECMT2110 Assumed knowledge: This unit relies on mathematical knowledge at the level of the Maths in Business program, including calculus and matrix algebra. Students who do not meet this requirement are strongly encouraged to acquire the needed mathematical skills prior to enrolling in this unit. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
Statistical analysis of quantitative data is a fundamental aspect of modern business. The pervasiveness of information technology in all aspects of business means that managers are able to use very large and rich data sets. This unit covers a range of methods to model and analyse the relationships in such data, extending the introductory methods in BUSS1020. The methods are useful for detecting, analysing and making inferences about patterns and relationships within the data so as to support business decisions. This unit offers an insight into the main statistical methodologies for modelling the relationships in both discrete and continuous business data. This provides the information requirements for a range of specific tasks that are required, e.g. in financial asset valuation and risk measurement, market research, demand and sales forecasting and financial analysis, among others. The unit emphasises real empirical applications in business, finance, accounting and marketing, using modern software tools.
QBUS2820 Predictive Analytics

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: QBUS2810 or ECMT2110 or DATA2002 Assumed knowledge: This unit assumes mathematical knowledge at the level of the Maths in Business program (including calculus and matrix algebra) and basic computer programming skills at the level of QBUS2810. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
Predictive analytics are a set of tools to enable managers to exploit the patterns found in transactional and historical data. For example major retailers invest in predictive analytics to understand, not just consumers' decisions and preferences, but also their personal habits, so as to more efficiently market to them. This unit introduces different techniques of data analysis and modelling that can be applied to traditional and non-traditional problems in a wide range of areas including stock forecasting, fund analysis, asset allocation, equity and fixed income option pricing, consumer products, as well as consumer behaviour modelling (credit, fraud, marketing). The forecasting techniques covered in this unit are useful for preparing individual business forecasts and long-range plans. The unit takes a practical approach with many up-to-date datasets used for demonstration in class and in the assignments.

3000-level units of study

QBUS3310 Advanced Management Science

Credit points: 6 Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: QBUS2310 Prohibitions: ECMT3610 or ECMT3710 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit gives guidelines for the formulation of management science models to provide practical assistance for managerial decision making. Optimisation methods are developed, and the complexity and limitations of different types of optimisation model are discussed so that they can be accounted for in model selection and in the interpretation of results. Linear programming methods are developed and extended to cover variations in the management context to logistics, networks, and strategic planning. Other topics may include decision analysis, stochastic modelling and game theory. The unit covers a variety of case studies incorporating the decision problems faced by managers in business.
QBUS3330 Methods of Decision Analysis

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: QBUS2320 or ECMT2630 or ENGG1850 or CIVL3805 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This introductory unit on decision analysis addresses the formal methods of decision making. These methods include measuring risk by subjective probabilities; growing decision trees; performing sensitivity analysis; using theoretical probability distributions; simulation of uncertain events; modelling risk attitudes; estimating the value of information; and combining quantitative and qualitative considerations. The primary goal of the unit is to demonstrate how to build models of real business situations that allow the decision maker to better understand the structure of decisions and to automate the decision process by using computer decision tools.
QBUS3340 Operations Management

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: QBUS2330 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit covers the fundamentals of operations management, an exciting area that has a profound effect on the productivity of both manufacturing and services. The techniques of operations management apply throughout the world to virtually all productive enterprises (i.e. offices, hospitals, restaurants, department stores and factories) - the production of goods and services requires operations management. The efficient production of goods and services requires the effective application of the concepts, tools, and techniques introduced in this unit. These include quality management, capacity planning, location and layout strategies, supply chain management and inventory control.
QBUS3350 Project Planning and Management

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prohibitions: QBUS2350 Assumed knowledge: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
Project management provides organisations with a powerful set of tools to improves their ability to plan, implement, and manage activities to accomplish specific organisational objectives. Project management is more than just a set of tools; it is a results-oriented management style that places a premium on building collaborations among a diverse cast of characteristics. This unit introduces students to the planning and management of projects by focusing on a variety of practical topics including project network, PERT, resource scheduling, learning curves, cost and time management in projects, and the use of project management support systems. It also discusses the organisational, leadership, cultural, technological challenges that project managers might face.
QBUS3820 Machine Learning and Data Mining in Business

Credit points: 6 Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/unit Prerequisites: QBUS2820 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/unit
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
Advances in information technology have made available rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualisation and the analysis of business and market data, providing the information requirements for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to empirical applications using modern software tools.
QBUS3830 Advanced Analytics

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: QBUS2810 or DATA2002 or ECMT2110 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit is designed to equip students with advanced tools for estimation and testing in relevant business statistical models. In particular, the unit covers maximum likelihood, Bayesian estimation and inference, and hypothesis testing. The unit acknowledges the importance of learning computing skills as helpful for job applications and special emphasis is made throughout the unit to learn numerical methods such as Monte Carlo simulations and Bootstrapping. Special topics in advanced statistical modelling, such as nonlinear estimators and time series regression, are also covered. The materials taught are essential as preparation for honours in Quantitative Business Analysis.
QBUS3840 Choice Modelling

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: QBUS2810 or DATA2002 or ECMT2110 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
How do business analysts model firm or consumer behaviour quantitatively How do analysts model brand choice in marketing or travel mode choice in transport These questions are answered by modelling choices with statistical tools designed for qualitative or discrete data such as logistic regressions rather than the standard linear regression models This unit investigates various quantitative modelling techniques relevant for choice modelling through business cases in marketing transport research strategy economics and other relevant business fields This unit also explores models that pool observations on a crosssection of households countries firms etc over several time periods This is known as panel data models which are increasingly relevant in all areas of Business with the growing availability of new sources of data
QBUS3850 Time Series and Forecasting

Credit points: 6 Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: QBUS2820 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
Time series and dynamic modelling is a fundamental component of modern business practice. Further, forecasting is a required component of business decision making. This unit provides an introduction to the time series models used for the analysis of data arising in different business areas including finance, accounting, marketing, economics and many other disciplines. It then considers methods for point and interval forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of time-series data; Seasonal Exponential smoothing and ARIMA models; Vector Autoregressions; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting risk measures such as Value at Risk and Expected Shortfall; dynamic factor models. Emphasis is placed on applications involving the analysis of many real business datasets. Students are encouraged to undertake hands-on analysis using appropriate software.
QBUS3600 Business Analytics in Practice

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: Student commencing from 2018: completion of at least 120 credit points including QBUS2310, QBUS2810 and QBUS2820. 2018 continuing students: completion of at least 120 credit points including QBUS2310 and QBUS2810 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This capstone unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills developed across the Business Analytics major. The problem-based approach to learning in this unit offers vital tools and techniques for business decision makers in the big data era through the use of very large and rich data sources. The unit casts the knowledge of statistical learning in a modern machine learning context and exposes business students to a range of state-of-the-art machine learning topics with the emphasis on applications involving the analysis of business data. Machine Learning is a fundamental aspect of business analytics that automates analytical modelling and decision making. Students ensure their career-readiness by demonstrating their ability to apply concepts, theories, methodologies, and programming skills to authentic problems and challenges faced in the field of business analytics.
QBUS3400 Industry and Community Project

Credit points: 6 Session: Intensive February,Intensive July,Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: 72 credit points Prohibitions: BUSS3110 or ACCT3400 or BANK3400 or CLAW3400 or FINC3400 or IBUS3400 or INFS3400 or MKTG3400 or WORK3400 or WORK3401 Assumed knowledge: (BUSS1020 or DATA1001 or ECMT1010 or ENVX1002 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH coded 1000-level units including MATH1905) and QBUS1040 and (QBUS2310 or QBUS2810) Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This interdisciplinary unit provides students with the opportunity to address complex problems identified by industry, community, and government organisations, and gain valuable experience in working across disciplinary boundaries. In collaboration with a major industry partner and an academic lead, students integrate their academic skills and knowledge by working in teams with students from a range of disciplinary backgrounds. This experience allows students to research, analyse and present solutions to a real-world problem, and to build on their interpersonal and transferable skills by engaging with and learning from industry experts and presenting their ideas and solutions to the industry partner.